Use of computing devices and software is enabling advanced analytics in areas where manual or spreadsheet-based computation were traditionally used. One example of an area that is seeing increasing computer-based analysis is workforce management/human resources (HR). To illustrate, an enterprise may maintain large databases to store data related to employee hiring, retention, resignation, etc. However, the data in the databases is historical data, which may not be applicable to future economic conditions. It may thus be difficult for an enterprise to predict, model, and plan for workforce changes and contingencies that can occur in the future. Spreadsheets may also be cumbersome and error-prone, thereby limiting planning frequency, increasing the number of people involved in the planning process (e.g., the number of people that need to have access to the spreadsheet), and diminishing the quality of planning results. Moreover, maintaining consistent labels for data in the spreadsheet may be difficult or cumbersome. Consistency may be even harder to enforce when certain aspects of a plan are delegated to different sub-planners who each may maintain a different spreadsheet that needs to be consolidated with the “main” spreadsheet at a later date. Further, when the spreadsheets are updated (e.g., when one or more rows of data values are added to the spreadsheets), it may be difficult to relabel all of the rows across all of the spreadsheets based on the received data. For example, a row of incoming data may have a first label, but a destination row in the spreadsheet for the incoming data may have a second label (and may additionally have different labels in other spreadsheets), making data reconciliation difficult.